JP2005181040A - Defect detection method for display panel, its detection device, and manufacturing method for the display panel - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a defect detection method for display panel capable of detecting at least latent defects, its device and a manufacturing method for the display panel. <P>SOLUTION: To a display panel of inspection objective, a second common voltage which is the sum of a first common voltage whose transmissivities for a specific first voltage and a second voltage match and a specific bias voltage is impressed in a process for special driving. A process for photographing the specially driven display panel and a process for detecting the existence of latent defects or evaluating the degree of the defect, based on the photographed inspection images are provided. Even if a (latent) defect is not detectable by impressing the first common voltage, the difference between the brightness for positive voltage and brightness for negative voltage becomes prominent, when the second common voltage, which is the sum of the first common voltage and the bias voltage, is impressed; because the balance of the transmissivity for the positive voltage and the transmissivity for the negative voltage collapses by a large amount, and the two are discriminated. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a display panel defect detection method, a detection apparatus for the same, and a display panel manufacturing method, which automatically detect a screen defect or the like in an inspection process of a liquid crystal panel or the like.

  One of the defects appearing on the screen of a liquid crystal display device is, for example, a so-called spot defect. A spot defect is a state where a certain area of the display screen has a difference in brightness from other areas, and is a state where there is a bright part or a dark part compared to the surrounding area within a narrow range. However, there is no strict definition and it is called a mura defect or a spot defect having a small defect size (hereinafter, simply referred to as a spot defect). Such a spot defect reduces the image quality, and is therefore an object for visual inspection of the display body. Conventionally, a visual inspection by a person is normal, but recently, an automatic inspection has been performed. There are many proposals for an automatic inspection method for this spot defect, for example, the following inspection method.

For example, “a photoelectric conversion input unit that inputs an image of an object and converts it into original image data, an image memory for storing the original image data, and a layer that creates inspection region data by adjusting the inspection region of the original image data Processing unit, two-dimensional statistical data processing unit for creating two-dimensional statistical data as a kind of texture analysis from inspection area data, and non-uniformity detection unit for extracting display unevenness defect from luminance distribution of two-dimensional statistical data And an inspection result display unit for displaying the inspection result on the screen ... "(Patent Document 1), and the defect is detected from the two-dimensional statistical data on the layered image. Detected.
JP-A-10-10007 (Claim 1 etc.)

  However, in the above detection method (Patent Document 1), it is impossible to distinguish a luminance variation caused by the influence of the light source of illumination or the influence of the imaging lens and the defect to be inspected or to detect a potential spot defect. There was a problem that it was not possible. The contents of the potential spot defect will be described in an embodiment described later.

  The present invention has been made in view of the above problems, and provides a display panel defect detection method and apparatus capable of at least detecting potential defects, and a display panel manufacturing method. The purpose is to do.

  According to the display panel defect detection method of the present invention, with respect to a display panel to be inspected, a predetermined common voltage is set for a first common voltage such that the transmittances of the predetermined first voltage and the second voltage are the same. Special driving for applying a second common voltage to which a bias voltage is applied, imaging the display panel, detecting the presence or absence of a defect based on the imaged inspection image, or the extent of the defect And a step of evaluating. Even if it is a defect (potential defect) that cannot be detected by applying the first common voltage that has the same transmittance in the predetermined positive voltage and negative voltage, it is biased with respect to the first common voltage. By applying the second common voltage to which the voltage is added, the balance between the transmittance due to the positive voltage and the transmittance due to the negative voltage is greatly lost, and the luminance value of the normal pixel and the luminance value of the potential defect are The difference is significant and the two can be identified. In the present invention, it is assumed that a defect including a potential defect is detected, but the latent defect means a defect that does not appear in normal driving (driving by the first common voltage). doing. This potential defect is expected to occur because, for example, the alignment film provided on the display panel is altered by ultraviolet rays, heat, or the like. Therefore, the defect (potential defect) has a degree of the defect. Since it progresses, it is also called a progressive defect. In contrast to this progressive defect, a defect detected by normal driving is called a non-progressive defect because its degree does not advance. The above-described potential defect will be described in detail in an embodiment described later (particularly, refer to FIGS. 3 to 6).

  In the defect detection method for a display panel according to the present invention, the special driving step sets the second common voltage to a value obtained by shifting the second common voltage to the positive voltage side or the negative voltage side with respect to the first common voltage. Apply. By setting and applying a value shifted to the positive voltage side or the negative voltage side with respect to the first common voltage as the second common voltage, the transmittance by the positive voltage and the transmittance by the negative voltage This shifts to the positive voltage side or the negative voltage side and greatly collapses, and the difference between the luminance value of a normal pixel and the luminance value of a potential defect becomes significant, and the two can be identified.

  In the defect detection method for a display panel according to the present invention, the special driving step includes shifting the value of the first common voltage to the positive voltage side and the negative voltage side as the second common voltage. The determined value is applied at a predetermined cycle (for example, a cycle of one screen to several screens). The balance between the transmittance due to the positive voltage and the transmittance due to the negative voltage shifts to the positive voltage side and the negative voltage side, respectively, so that the difference between the luminance value of the normal pixel and the luminance value of the potential defect is remarkable. Thus, both can be identified.

  In the defect detection method for a display panel according to the present invention, the step of detecting the presence or absence of the potential defect or evaluating the degree of the defect targets a spot defect, and detects the potential spot defect with high accuracy or It is possible to evaluate the degree.

  The defect detection method for a display panel according to the present invention further includes a step of applying the first common voltage to the display panel to be inspected to perform normal driving, and no defect is detected in the normal driving. When a defect is detected in the special drive, the defect is set as a potential spot defect. By performing both the defect detection by the normal drive and the defect detection by the special drive, it is possible to identify a potential spot defect.

  In the defect detection method for a display panel according to the present invention, the step of detecting the presence or absence of the potential defect or evaluating the degree of the defect includes spot defect enhancement of the inspection image for each of the normal drive and the special drive. And performing a filtering process for detecting the presence or absence of a spot defect or evaluating the degree of the spot defect based on the filtered inspection image. Since the detection process of the presence or absence of a spot defect is performed after the filter process for spot defect enhancement is performed, it is possible to detect the spot defect with high accuracy.

  In the defect detection method for a display panel according to the present invention, the step of detecting the presence or absence of the potential defect or evaluating the degree of the defect includes the imaging inspection object for each of the secondary drive and the special drive. Capturing a screen image of the image, taking a difference from the background image created in advance from the captured image, creating a background difference image, performing a process of flattening the background difference image, A step of creating a plurality of stages of reduced images from the flattened image, and performing the filtering process using each of the reduced images as an inspection image. In the present invention, in order to be able to detect from a small defect defect having a large contrast and a large size, first, the screen of the inspection object is imaged, and the difference between the image and the background image is taken as the inspection object. An inspection image which is a background difference image from which a luminance change caused by other than the above is removed is created. Then, the inspection image is flattened, and image size reduction processing is performed to reduce the image size from the flattened image over a plurality of stages. By this reduction processing, various large and small spot defects in the inspection image are reduced, and as a result, a defect of a size that can be detected exists in any of the plurality of reduced images. Next, the contrast of the spot defect is enhanced by performing filter processing for defect enhancement on each of the reduced images. Therefore, a spot defect can be detected with high accuracy regardless of the size of the defect or the level of contrast.

  In the defect detection method for a display panel according to the present invention, the step of detecting the presence / absence of the defect and evaluating the degree of the defect is performed in the image after the filtering process for each of the secondary drive and the special drive. Determining the luminance data of each pixel, determining a threshold based on the luminance statistical data, and extracting defect candidates based on the threshold. A threshold for determining the presence or absence of a spot defect and extracting a defect candidate is automatically determined based on the brightness statistical data by calculating the brightness data of each pixel in the detected image. Therefore, an optimum threshold value can be obtained according to the image to be inspected, and thereby a spot defect can be detected with high accuracy.

  In the defect detection method for a display panel according to the present invention, the image data of the screen to be inspected is 12-bit data having 4096 gradations or more. By using such high-resolution image data, the accuracy of the luminance statistical data is increased, so that it is possible to further improve the defect detection accuracy. In addition, the accuracy of evaluation values of defect candidates described later is also improved.

  In the defect detection method for a display panel according to the present invention, the background image is obtained by averaging a plurality of images taken by the same optical system and the same imaging system. The background image is created in order to remove the luminance change caused by other than the inspection target and extract the defect of the inspection target. Therefore, the background image is an image of only the luminance change caused by other than the inspection target, and the same optical system and the same imaging system are used to capture a plurality of display screens with as few defects as possible, and the average of the captured images is averaged. As a result, the component of the defective part of the inspection target randomly present in the image is weakened, and only the luminance change caused by other than the inspection target such as the screen, illumination, lens characteristics, etc. always exists in the same position in the image remains. A background image is obtained.

  In the display panel defect detection method according to the present invention, before the background difference image is created, a display area of the background difference image is extracted, and the display area is geometrically transformed into a rectangle. For example, when the inspection image is, for example, an image projected on the screen by a projector, for example, an edge portion of the screen is included in the image, or a display area portion is inclined with respect to the screen. Sometimes. Therefore, when creating an inspection image, the display area is extracted from the image including the screen to be inspected, and the display area is geometrically deformed to make a rectangle, thereby distorting the image as described above. Deformation and the like can be corrected.

  In the defect detection method for a display panel according to the present invention, the threshold value is obtained corresponding to a light defect and a dark defect, respectively. In order to be able to detect both the white spot defect and the black spot defect, the threshold value of the defect candidate is determined corresponding to the bright defect and the dark defect. For example, the white spot threshold value for extracting a bright defect can be obtained from the following formula: average brightness data + a1 × standard deviation, and the black spot threshold value for extracting a dark defect can be obtained from the following formula: average brightness data−a2 × standard deviation. . The calculation formulas a1 and a2 are predetermined constants.

  In the defect detection method for a display panel according to the present invention, the threshold value is obtained using an average value and a standard deviation of the luminance statistical data. Therefore, defect candidates can be objectively and quantitatively evaluated, and accurate evaluation can be performed.

  The defect detection method for a display panel according to the present invention further includes a step of obtaining a characteristic value of the defect candidate by blob processing and calculating an evaluation value based on the characteristic value of the defect candidate and the luminance statistical data. . Since the characteristic value of the defect candidate is obtained by blob processing, and the evaluation value of the defect candidate is calculated by a predetermined formula based on this characteristic value and the statistical data, the degree of the defect candidate can be quantitatively evaluated.

  In the defect detection method for a display panel according to the present invention, the evaluation value of the defect candidate is obtained for each of the bright defect and the dark defect. Evaluation values of the defect candidates are obtained for both bright and dark defects.

  In the defect detection method for a display panel according to the present invention, the evaluation value of the defect candidate is obtained using an average value and a standard deviation of the luminance statistical data, and a maximum luminance and a minimum luminance of the defect candidate.

  The display panel defect detection method according to the present invention classifies a good product rank according to the evaluation value of the defect candidate. Therefore, the spot defect can be objectively evaluated for each product (including parts), and the spot defect can be ranked and the product can be graded. These statistical data can be used for quality control.

  The defect detection apparatus for a display panel according to the present invention applies to a display panel to be inspected and a display panel to be inspected with respect to a first common voltage such that transmittances at a predetermined positive voltage and negative voltage coincide with each other. A drive unit that specially drives by applying a second common voltage to which a bias voltage is applied, an imager that images the specially driven display panel, and the presence or absence of a potential defect based on the imaged inspection image Arithmetic means for detecting or evaluating the degree of the defect. This calculation means performs calculation processing for detecting a potential defect or evaluating the degree of the defect in the defect detection method described above.

  The display panel manufacturing method according to the present invention includes the above-described display panel defect inspection method in a manufacturing process, and is capable of manufacturing a display panel subjected to defect inspection with high accuracy.

Embodiment 1. FIG.
FIG. 1 is a configuration diagram of a liquid crystal panel defect detection apparatus and related equipment according to Embodiment 1 of the present invention. In the first embodiment, a dark box 10 is provided. A screen 11 is provided on one side of the dark box 10, and openings 10a and 10b are provided on the other side. A liquid crystal panel (also referred to as a TFT panel or a liquid crystal light valve) 12 using a TFT element to be inspected is disposed to face the opening 10b, and the liquid crystal panel 12 is irradiated with light from the projector light source 13. An image signal or the like for drawing a predetermined pattern is given to the liquid crystal panel 12 by the signal generator 14, and the drawn image is enlarged by the projection lens 15 and displayed on the screen 11. For example, a CCD camera (hereinafter referred to as a camera) 16 is disposed opposite to the opening 10a of the dark box 10 as an imaging means. The camera 16 captures an image displayed on the screen 11, and the image signal is A / D. An analog signal is converted into a digital signal by a converter (not shown), and is taken into the computer 17. At this time, the image data is represented by a luminance value of 4096 gradations, for example, 12-bit data with “0” for black and “4095” for white for each pixel by the A / D converter.

  The computer 17 includes a calculation means (CPU) 17a and a storage means 17b. The storage means 17b stores the image data converted into a digital signal in the storage means 17b and also stored in the storage means 17b. By processing the image data by a method to be described later, a spot defect (normal spot defect and potential spot defect) is detected for each bright / dark defect. In defect detection, after reducing images with multiple reduction sizes and filtering processing for spot defect enhancement (stain defect enhancement processing), statistical processing of luminance information in the detected image is performed, and the statistical data is used as a basis. Then, a threshold value for extracting defect candidates is determined to extract defect candidates, and further, an evaluation value for quantitatively evaluating the extracted defect candidates is calculated and obtained. These inspection results are displayed on the display means 18 as output means. In addition, when the liquid crystal panel 12 is driven to detect the above-described spot defect, the arithmetic unit 17a draws an image by a special drive as well as a normal drive to cause a potential spot defect of the liquid crystal panel 12. Also detect about. Details of these normal drive and special drive will be described with reference to FIGS.

  FIG. 2 is an equivalent circuit diagram focusing on one pixel of the liquid crystal panel 12. A driving voltage (control voltage) Vs is supplied to the source of the TFT (thin film transistor) 20, and this driving voltage is a pulse voltage (AC voltage) that vibrates positively and negatively at a predetermined cycle. The drain of the TFT 20 is connected to a common electrode (not shown) via the liquid crystal cell 22, and a common voltage Vcom is supplied to the common electrode. The common voltage Vcom is adjusted so that the transmittances coincide with each other at a predetermined setting voltage (that is, between the first voltage and the second voltage) in a normal driving state. However, in this embodiment, an example of zero voltage will be described. In the case of special driving, a common voltage (second common voltage) Vcom obtained by adding a predetermined voltage (bias voltage) to the common voltage (first common voltage) is supplied. In the actual liquid crystal panel 12, gradation display is controlled by the value (magnitude) of the drive voltage Vs. Note that the above set voltages (first voltage, second voltage) do not necessarily take positive / negative values, and have a predetermined voltage amplitude on the positive and negative sides with respect to a predetermined reference voltage. There are some examples, and all of them may take a positive value.

  FIG. 3 is a characteristic diagram showing the relationship between the drive voltage Vs and the transmittance T of a normal pixel during normal driving and a pixel having a potential spot defect. Here, the common voltage (first common voltage) Vcom is zero voltage, and the transmittance of a normal pixel is symmetric with respect to the common voltage Vcom, but the transmission of a pixel having a potential spot defect is present. The rate is slightly deviated toward the negative voltage side with respect to the common voltage Vcom. In other words, the transmittance is biased between white and black at the rise (negative voltage) and fall (positive voltage). The reason why a pixel having a potential spot defect shows a characteristic different from that of a normal pixel is considered to be because, for example, the alignment film is deteriorated by ultraviolet rays, heat, impact, or the like. However, since the period in which these characteristics occur is extremely short, the points that can be identified are averaged points, and the points indicated as “obtained transmittance” in the characteristics of FIG. 3 are the values of the corresponding pixels. Since the difference from the adjacent normal element (pixel) is small, it cannot be identified. In this way, defects that cannot be detected during normal driving are referred to as potential spot defects.

  FIG. 4 is a characteristic diagram showing the relationship between the drive voltage Vs and the transmittance T of a normal pixel and a pixel having a potential spot defect during special driving. Here, as the common voltage (second common voltage) Vcom, a predetermined voltage (for example, about 0.1 V to 0.2 V) is added to the positive voltage side (a bias voltage is added), and a normal pixel is obtained. In addition, the transmittance of the pixel having the potential spot defect is asymmetric with respect to the common voltage Vcom, and the difference in the average value of the transmittance is enlarged, so that a difference ΔT appears. Since the difference ΔT can distinguish between a normal pixel and a pixel having a potential spot defect, a pixel having a potential spot defect can be detected.

  FIG. 5 is a characteristic diagram showing the relationship between the drive voltage Vs and the transmittance T of normal pixels during normal driving, and pixels with white spots and black spots. Here, the common voltage Vcom is a zero voltage, the transmittance of normal pixels, pixels with white spots and black spots are symmetric with respect to the common voltage Vcom, Since the luminance value is different from the transmittance of pixels with white spots and black spots, and the difference ΔT is also large, it is possible to distinguish between normal pixels, pixels with white spots and black spots. Thus, it is possible to detect pixels with white spots and black spots.

  FIG. 6 is a characteristic diagram showing the relationship between the drive voltage Vs and the transmittance T of a normal pixel during special driving, a pixel with white spots and black spots. The normal pixel transmittance and the transmittance of pixels with white spots and black spots show different luminance values, and the difference ΔT is also large. Therefore, normal pixels, pixels with white spots and black spots Can be distinguished from each other, and pixels having white spots and black spots can be detected.

  As is apparent from the description of FIGS. 3 to 6, detection of pixels with white spots and black spots is detected in both normal driving and special driving, and pixels with potential spot defects are detected in normal driving. Instead, it is detected by special driving. Therefore, a potential spot defect is defined as a potential spot defect when an inspection image in which a defect is not detected in normal driving is detected by special driving. Now that the operation principle in the first embodiment has been clarified, the operation of the spot defect detection apparatus in FIG. 1 will be described with reference to FIG.

  FIG. 7 is a flowchart mainly showing the processing progress of the calculation means 17a of FIG. 1, which is automatically performed by the inspection program stored in the storage means 17b. FIG. 8 is a flowchart showing details of the processes (S6) to (S8) and (S13) to (S16) of FIG. 7, and FIG. 9 is an explanatory view showing the transition of the image in the above process.

(1) A set of liquid crystal panels (S1).
The liquid crystal panel 12 is set at a predetermined position, the projector light source 13 is turned on, and preparations for projecting an image drawn on the liquid crystal panel 12 onto the screen 11 are made.
(2) Transmission of normal drive signal (S2)
The arithmetic means 17a transmits a control signal to the signal generator 14 and causes the liquid crystal panel 12 to transmit a control signal for normal driving. That is, as shown in FIGS. 3 and 5, the signal generator 14 sets the common voltage (first common voltage) Vcom to zero voltage, and also causes the liquid crystal panel 12 to draw a predetermined image. Image control signal) Vs is supplied. The liquid crystal panel 12 drives the TFT element 20 based on the supplied drive voltage Vs, applies a voltage to the liquid crystal cell 22, and causes the liquid crystal panel 12 to draw an image. Here, the entire display unit of the liquid crystal panel 12 is displayed in halftone (for example, 128 when the transmittance is 256 steps). The image drawn on the liquid crystal panel 12 is enlarged and projected onto the screen 11. The image projected on the screen 11 is picked up by the camera 16. At the time of imaging, the luminance level is adjusted (exposure time and aperture) so that the display surface is not saturated (exceeds the maximum luminance value of the camera 16).
(3) Capture of normal drive image (S3)
The computing means 17a captures an image signal photographed by the camera 16, and the image signal is converted from an analog signal to a digital signal by an A / D converter (not shown) and captured by the computer 17. At this time, the image data is represented by a luminance value of 4096 gradations, for example, 12-bit data with “0” for black and “4095” for white for each pixel by the A / D converter.

(4) Display area extraction process (S4)
The computing unit 17a performs a display area extraction process for the image (image data) stored in the storage unit 17b. The display area extraction processing refers to extracting only the screen portion to be inspected from the input image captured by imaging. For example, FIG. 9A shows an input image at the time of imaging. As shown in this figure, the input image 41 at the time of imaging includes an image 11 a corresponding to the edge portion of the screen 11, and the display area image 42 corresponding to the display screen portion is not exactly rectangular but the screen 11. May be obliquely distorted. This is due to the fact that the screen 11 and the camera 16 are not exactly parallel, or that distortion is caused by the characteristics of the projection lens 15 and the lens characteristics of the camera 16. In addition, as described above, the input image may be distorted or deformed even when an image of the liquid crystal panel 12 or the like is directly captured instead of indirect imaging. Of course, when the camera 16 and the liquid crystal panel 12 screen to be inspected are accurately set so that the image 11a corresponding to the edge portion as described above does not enter, for example, the detection target is within the field of view of the camera 16, for example. When the field of view is accurately set so that the entire screen portion can be accommodated, the display area extraction process and the correction process described below can be omitted, and the image captured by the imaging is directly converted into the original image. It becomes.

  When the input image 41 is distorted as shown in FIG. 9A, only the display area image 42 of the screen portion is extracted as shown in FIG. A display area correction image 43 corrected to be an accurate rectangle by applying geometric deformation is created. This display area correction image 43 is an original image to be actually inspected here. In this image correction processing by geometric deformation, the coordinates of the four corners of the display area image 42 not including the image 11a corresponding to the edge portion of the screen 11 are detected by pattern matching processing, and the coordinates of the four corners of the rectangle are detected. This is done by converting the coordinates to match the coordinates. By setting the rectangular size set at this time to, for example, a 1200 × 1000 pixel size, the image size of the original image 43 becomes 1200 × 1000 pixels. This size is not particularly fixed and may be set to a size that is equal to or close to the pixel size of the camera 16.

(5) Background image difference processing (S5)
The computing unit 17a performs background image difference processing on the display area extracted image. The background image difference process is a process of subtracting a previously created background image 44 as shown in FIG. 9C from the original image 43 created as described above. The background image 44 is obtained by, for example, capturing about 20 images of a liquid crystal panel with few defects with the apparatus configuration of the present invention (configuration including the same optical system and the same imaging system), and averaging the images in advance. It is created by the same method as the original image 43 and stored in the storage means 17b. By subtracting the background image 44 from the original image 43, changes in brightness caused by the screen other than the inspection target included in the original image 43, the illumination of the imaging means, the characteristics of the projection lens 15 of the projector, and the like are removed. be able to. In this difference process, 2048 (4096 × 1/2) is added as an offset value so that the luminance data does not become a negative value. As a result of the background image difference processing, a background difference image, that is, an inspection image 45 as shown in FIG. 9D is obtained. Then, if there are, the background difference image (inspection image) 45 includes a spot defect 50 having a different size and contrast or a mura defect 51 having a relatively large size.

(6) Flattening process (S6)
The background difference image (inspection image) 45 has a large luminance change that occurs in the entire display area, that is, a change in brightness such that there is a bright place or a dark place over a wide range that is not a problem for humans, Thus, in order to exclude the relatively large uneven defect 51 and the like from the detection target, the calculation unit 17a performs a flattening process on the background difference image, that is, the inspection image 45. . This flattening process removes large brightness changes such as illuminance unevenness and large-size uneven defects, so that only relatively small-sized stain defects and stain defects with different contrasts remain. A flattened image 46 as shown in FIG. The flattening process is a process for smoothing by a process using a smoothing filter or a morphology process.

(7) Image size reduction processing (see S7, S7a to S7e in FIG. 8)
Although the size of the spot defect is small, there are various sizes of the spot defect, and the spot defect has a high contrast and a low contrast. Since a filter described later can only emphasize a spot defect of a predetermined size, a process of reducing the image size of the flattened image 46 in a plurality of stages in order to correspond to the spot defects of various sizes existing in the flattened image 46. I do. Here, from the flattened image 46 of the original image 1200 × 1000 pixels, 1/2 (600 × 500 pixels), 1/4 (300 × 250 pixels), 1/8 (150 × 125 pixels), 1/16 ( Five reduced images reduced to five levels of 75 × 62 pixels) and 1/32 (38 × 31 pixels) are created.

  FIG. 10 is an explanatory diagram of a method for reducing the image size. In the image size reduction method, as shown in the figure, the average value of the luminance data for four pixels of the original image is assigned to one pixel of the new image to reduce it to ½ size. By repeating this method, images of 1/2, 1/4, 1/8, 1/16, and 1/32 scale sizes are obtained. In the first embodiment, these reduced images are used as inspection images, and the below-described spot detection process is performed.

(8) Blemish enhancement processing (see step S8, S8a to S8e in FIG. 8)
This spot emphasis process is a pre-process (stain defect emphasis process) for making it easy to determine whether or not there is a spot in the reduced image as described above, and uses a filter as described below. To process. This process is performed for each reduced image. As the stain enhancement process, in the first embodiment, the contrast of a stain defect is enhanced using a tophat filter as a spatial filter for each reduced image. The top hat filter is a filter that performs contrast emphasis, and is composed of, for example, 7 × 7 pixels as shown in FIG. 11. By performing a convolution operation on the target pixel with this filter configuration value, a background is formed in the pixel. When a bright point (here, white spot defect) having a size of about 3 × 3 pixels is present, this is emphasized. However, the result of emphasizing the bright spot with the top hat filter is a positive value, so it is effective for detecting the bright defect (white spot), but the result of highlighting the dark spot (here, black spot defect) is negative. Since the image processing image format normally takes only a positive value, the result is replaced with 0, and there is a difficulty in detecting dark defects (black spots). Therefore, by performing a convolution operation in the top hat filter process and adding a value of “2048” as an offset value, the emphasized portion of the dark point, which is originally a negative value, is added to the plus, and as a result, only the top hat filter process is performed. Thus, it is possible to emphasize bright and dark points. Of course, the spatial filter is not limited to the top hat filter, and the result of emphasizing the dark spot is a positive value (the sign of the top hat filter is reversed). Well filter (see FIG. 12) May be used. Further, the filter size, weighting, etc. of these spatial filters are not limited to those shown in the figure.

  FIG. 9 (f) schematically shows a detection image 47 when the above-described top hat filter processing is performed on a reduced image (however, for the sake of clarity, the image sizes are shown in the same size). (Actually reduced size). By performing the top hat filter process and the offset process at the same time as described above, the result of emphasizing white spots and black spots can be obtained at a time, and the spot defect 50 can be easily detected. In addition, with respect to a spot defect having a size in which the defect is not emphasized in FIG. 9F, the defect is emphasized by a similar method in other reduced images that have become sizes that can be emphasized by being reduced in several steps. Will be.

(9) Defect determination process (S9)
Here, as the defect determination processing, statistical calculation (S21a to S21e), check 1 (S22), blob processing (S23a to S23e), and check 2 (S24) are performed.
(9.1) Statistical calculation (S21a to S21e)
Next, statistical data based on the luminance value of each pixel in the detection image 47 is calculated. In the calculation of statistical data, the average value Lave, standard deviation σ, maximum value Lmax, and minimum value Lmin of the luminance values in the entire image are obtained. From these four luminance statistical data, the threshold values for white spots and black spots are determined, for example, as follows.
White spot threshold: Lave + a1 × σ
Black spot threshold: Lave-a2 × σ
Here, a1 and a2 are predetermined constants.
Therefore, the threshold for detecting white spots and black spots can be automatically determined based on the statistical brightness data by statistically calculating the brightness data in the detected image 47. Therefore, the threshold is not artificial or trial and error, but is objective and relative.

(9.2) Check 1: Determination of presence / absence of defect candidate (S23)
Then, a first check is performed based on the threshold value to determine whether there is a white spot defect or a black spot defect candidate in the detected image 47. That is, if the maximum luminance value Lmax obtained by the statistical data calculation exceeds the white spot threshold, it is determined that there is a white spot defect in the detected image 47, and the minimum luminance value Lmin is equal to or less than the black spot threshold. If so, it is determined that there is a black spot defect in the detected image 47. If there is no defect candidate, the product is determined to be non-defective at this stage, and the inspection is terminated.

(9.3) Blob processing (S23a to S23e)
When it is determined in the check 1 that there is a defect candidate in the detected image 47, the defect candidate is extracted using the threshold value, and the blob process is performed to calculate the characteristic value of the defect candidate. A blob is a “chunk” having a value in a specific range existing in an image, and here is a defect candidate that is a region above the white spot threshold or a region below the black spot threshold. Accordingly, defect candidates are extracted by binarization processing using the white spot threshold and the black spot threshold, and the extracted region is subjected to blob processing, which is an image processing method, to calculate the characteristic values of the defect candidates. To do. Here, as the characteristic value of the defect candidate, the barycentric position X and Y coordinates of the area and the maximum brightness value (Lmax (n)) in the area if it is a white spot defect candidate, and in the area if it is a black spot defect candidate The minimum value of luminance (Lmin (n)) is obtained.

(9.4) Check 2: Calculation of evaluation value (S24)
In this second (ie, final) check, evaluation values are calculated for the defect candidates extracted as defect candidates in order to quantify the degree of defects. The evaluation values are the luminance statistical data (average value Lave (i), standard deviation σ (i), i is the screen number of the reduced image) obtained for each reduced image and the characteristic value (maximum) obtained by blob processing. Using the value Lmax (n), the minimum value Lmin (n), and n is a blob number), an evaluation value is calculated by the following equation.
White stain:
Ev (n) = k (i) × (Lmax (n) −Lave (i)) / σ (i)
Black stain:
Ev (n) = k (i) × (Lave (i) −Lmin (n)) / σ (i)
Where k (i) = reduced screen coefficient i = screen number Lmax (n) = maximum brightness of defect candidate n = blob number Lmin (n) = minimum brightness of defect candidate Lave = average brightness of entire image
σ = Standard deviation of luminance of the entire image By obtaining the evaluation value using these formulas, objective data including white spot defects and black spot defects extracted as defect candidates, along with their coordinate position and number (blob number) In addition to determining the presence or absence of white spot defects and black spot defects, the rank based on the degree of defects can be determined. Therefore, the spot defect detection accuracy is high.

  With respect to this evaluation value, the threshold for classifying the rank can be set in several steps for each non-defective product rank. As a result, it is possible to rank the non-defective product with stains and grade the product. For example, when the liquid crystal panel is used as a light bulb for a projector for each of R (red), G (green), and B (blue) colors, the relative luminous sensitivity is most green (when the wavelength λ = 555 nm). Since it is high, the threshold for setting the rank of the green light valve is set to the strictest value and the value is set smaller than the other cases.

(10) Detection of potential spot defects by special driving (S10 to S17)
The arithmetic means 17a transmits a control signal to the signal generator 14 to supply a control signal for causing the liquid crystal panel 12 to perform special driving. The signal generator 14 supplies a common voltage (second common voltage) Vcom and a driving voltage Vs as shown in FIG. 4 to the liquid crystal panel 12 (S10). The liquid crystal panel 12 drives the TFT element 20 based on the supplied drive voltage and applies a voltage to the liquid crystal cell 22 to draw an image. The image drawn on the liquid crystal panel 12 is projected on the screen 11. The image projected on the screen 11 is taken by the camera 16. Thereafter, the above-described normal drive image capture (S3), display area extraction processing (S4), background image difference processing (S5), averaging processing (S6), size reduction processing (S7), and spot enhancement processing (S8) And processing having the same contents as those of the defect judgment processing (S9), special drive image capture (S11), display area extraction processing (S12), background image difference processing (S13), averaging processing (S14), The size reduction process (S15), the spot emphasis process (S16), and the defect determination process (S17) are performed.

  However, in the defect determination process (S17), when a defect defect is detected by special driving in the above-described defect determination process by normal driving (a defect image that could not be detected in S9). The defect is determined to be a potential spot defect.

(12) The determination results in the defect determination processes (S9) and (S17) are stored in the storage unit 17b and displayed on the display unit 18.

  Since the first embodiment is configured as described above and can detect a potential spot defect that cannot be detected by normal driving, the accuracy of spot defect detection of the liquid crystal panel is drastically improved. It has improved. In addition, spot defects existing on the LCD panel screen can be automatically detected with high accuracy regardless of the size of the defect, regardless of the contrast level, and the spot defects can be quantitatively detected individually. Can be evaluated. In addition, since spot defects are evaluated based on luminance statistics data, quality data on products and parts can be collected and analyzed, which can be used for quality control and a method aimed at further improving quality It is also possible to do. In addition, the defect inspection of the first embodiment is performed in the manufacturing process of the liquid crystal panel, and a highly accurate defect inspection is performed to obtain a quality-guaranteed liquid crystal panel.

Embodiment 2. FIG.
In the first embodiment described above, the example in which the second common voltage is biased to the positive voltage side with respect to the first common voltage has been described. Can be detected. Further, as the second common voltage, a value shifted to the positive voltage side and a value shifted to the negative voltage side with respect to the first common voltage are set and applied in a predetermined cycle (at least one screen cycle). You may make it do.

Embodiment 3. FIG.
In the first embodiment, an example of detecting a potential spot defect has been described. However, since the present invention detects each potential defect by focusing on individual pixels, only the spot defect is detected. In addition, the present invention is similarly applied to detection of point defects, streak defects, uneven defects, line defects, pixel uneven defects, and the like. For example, when detecting a point defect, a top hat filter or well filter is applied to the inspection image to emphasize the light point defect and the dark point defect, and at the same time the light point defect and the dark point defect are emphasized. The approximate center value of the gradations being added is added as an offset value, and light and dark defects are extracted based on the offset value, and the evaluation value is calculated. Also, when detecting streak defects, line detection filters having different emphasis angles in four stages so as to correspond to streaks in various directions with respect to each reduced image reduced in the same manner as described above. The streak defect is emphasized, and at the same time as the streak defect is emphasized, the approximate center value of the gradation displaying the image is added as an offset value, and the streak defect is extracted based on the value and the evaluation value is calculated. .

  Further, when detecting a mura defect, the inspection image or a reduced image obtained by reducing the inspection image is subjected to flattening processing of a plurality of stages to create first and second flattened images, and the inspection image Alternatively, difference processing is performed between the reduced image of the inspection image and the first flattened image and between the first flattened image and the second flattened image, and the images are displayed simultaneously. The approximate median value of the gradations to be added is added as an offset value, and streak defects are extracted based on the added value and the evaluation value is calculated. In addition, when detecting a line defect, the inspection image is flattened, and the line defect is emphasized by applying an edge detection filter for emphasizing the horizontal and vertical edges to the flattened image. At the same time as emphasizing the line defect, the approximate median value of the gradation displaying the image is added as an offset value, and a streak defect is extracted based on the value and its evaluation value is calculated. Further, when detecting a pixel unevenness defect, expansion processing is performed on each pixel of the inspection image, and a Sobel filter or a Laplacian filter is applied to the expanded image to emphasize the luminance difference between adjacent pixels. Thereafter, the same processing is performed as in the above example.

Embodiment 4 FIG.
In the first embodiment, the example in which the image to be inspected is reduced in a plurality of stages and the filter having the same size is applied has been described. Conversely, without reducing the image to be inspected, A filter having a plurality of sizes may be prepared and applied. In the first embodiment, a method of enlarging and displaying an image of a liquid crystal panel to be inspected by a projector and capturing and inspecting the enlarged image is employed. You may make it test | inspect by imaging directly.

  In the first embodiment, an example in which a potential spot defect is detected based on both normal drive and special drive inspection images has been described. However, a normal defect (white spot, black spot, etc.) is detected only by special drive. ) And potential spot defects may be detected as spot defects.

  The inspection object of the present invention is not limited to the liquid crystal panel using the TFT element as described above, but a liquid crystal panel using another diode element, a plasma display, an organic EL display, a DMD (direct mirror mirror), and the like. It can be used for inspection of display body parts such as devices) and display devices and products using them, and it goes without saying that even if used for these, they are not excluded from the scope of the present invention. Absent. Further, the filter used in the present invention is not limited to the one shown in the first embodiment, and can be appropriately changed within the scope of the object of the present invention.

1 is a configuration diagram of a spot defect detection device according to Embodiment 1 of the present invention. FIG. The equivalent circuit diagram of one pixel of a liquid crystal panel. The characteristic view which showed the relationship between the drive voltage Vs of normal drive, and the transmittance | permeability T. FIG. The characteristic view which showed the relationship between the drive voltage Vs of special drive, and the transmittance | permeability T. FIG. The characteristic view which showed the relationship between the drive voltage Vs of normal drive, and the transmittance | permeability T. FIG. The characteristic view which showed the relationship between the drive voltage Vs of special drive, and the transmittance | permeability T. FIG. The flowchart which shows the detection process of a spot defect. The flowchart which showed the detail of the flowchart of FIG. The schematic diagram which showed the transition of the image in said process. Explanatory drawing of the reduction method of image size. The block diagram of a top hat filter. The block diagram of a well filter.

Explanation of symbols

10 dark box, 11 screen, 12 liquid crystal panel, 13 projector light source, 14 signal generator, 15 projection lens, 16 camera, 17 computer, 17a computing means, 17a storage means, 18 display means.

Claims (20)

For the display panel to be inspected, a second common voltage obtained by adding a predetermined bias voltage to the first common voltage such that the transmittances at the predetermined first voltage and the second voltage match each other. A process of applying a special drive, and
Imaging the specially driven display panel;
A defect inspection method for a display panel, comprising: detecting presence or absence of a defect based on the imaged inspection image or evaluating a degree of the defect.   2. The special driving step includes setting and applying a value shifted to a positive voltage side or a negative voltage side with respect to the first common voltage as the second common voltage. The display panel defect inspection method described.   The special driving step includes applying, as the second common voltage, a value shifted to the positive voltage side and a value shifted to the negative voltage side with respect to the first common voltage at a predetermined cycle. The display panel defect inspection method according to claim 1, wherein:   The defect inspection method for a display panel according to claim 1, wherein the step of detecting the presence or absence of the potential defect or evaluating the degree of the defect targets a potential spot defect. .   The method further includes the step of applying the first common voltage to the display panel to be inspected to perform normal driving, and the defect is not detected in the normal driving and the defect is detected in the special driving. 5. The display panel defect inspection method according to claim 4, wherein the defect is a potential spot defect. The step of detecting the presence or absence of the potential defect or evaluating the degree of the defect, for each of the normal drive and the special drive,
A step of performing a filter process for spot defect enhancement of the inspection image,
6. The display panel defect inspection method according to claim 5, wherein the presence or absence of a spot defect is detected or the degree of the spot defect is evaluated based on the filtered inspection image. The step of detecting the presence or absence of the potential defect or evaluating the degree of the defect, for each of the normal drive and the special drive,
Taking the image of the image of the screen to be inspected and creating a background difference image by taking the difference from the background image created in advance from the captured image;
Performing a flattening process on the background difference image;
And further comprising a step of creating a reduced image of a plurality of stages from the flattened image,
The display panel defect inspection method according to claim 6, wherein the filtering process is performed using each of the reduced images as an inspection image. The step of detecting the presence or absence of the latent defect and evaluating the degree of the defect, for each of the normal drive and the special drive,
Obtaining statistical data of luminance values of each pixel in the image after the filtering process;
8. The display panel defect inspection method according to claim 7, further comprising: determining a threshold value based on the luminance statistical data, and extracting a defect candidate based on the threshold value.   9. The display panel defect inspection method according to claim 7, wherein the image data of the screen to be inspected is data of 4096 gradations of 12 bits or more.   The display panel defect inspection method according to claim 7, wherein the background image is obtained by averaging a plurality of images captured by the same optical system and the same imaging system. .   11. The method according to claim 8, further comprising: extracting a display area of the inspection image before making the background difference image, and performing geometric deformation on the display area to make a rectangle. A display panel defect inspection method according to claim 1.   12. The display panel defect inspection method according to claim 8, wherein the threshold values are respectively determined corresponding to a bright defect and a dark defect.   13. The display panel defect inspection method according to claim 12, wherein the threshold value is obtained using an average value and a standard deviation of the luminance statistical data.   In the step of detecting the presence or absence of the latent defect and evaluating the degree of the defect, a characteristic value of the defect candidate is obtained by blob processing, and an evaluation value is calculated based on the characteristic value of the defect candidate and the luminance statistical data 14. The display panel defect inspection method according to claim 8, further comprising a step of:   15. The defect detection method for a display panel according to claim 14, wherein the evaluation value of the defect candidate is obtained for each of a bright defect and a dark defect.   16. The defect inspection of a display panel according to claim 14, wherein the evaluation value of the defect candidate is obtained by using an average value and a standard deviation of the luminance statistical data, and a maximum luminance and a minimum luminance of the defect candidate. Method.   17. The display panel defect detection method according to claim 14, wherein a non-defective product rank is classified according to the evaluation value of the defect candidate. Driving means for applying special driving to the display panel to be inspected by applying a second common voltage obtained by adding a bias voltage to the first common voltage having the same transmittance at a predetermined positive voltage and negative voltage When,
Imaging means for imaging the specially driven display panel;
A display panel defect inspection apparatus comprising: a calculation unit that detects the presence or absence of a potential defect based on the captured inspection image or evaluates the degree of the defect.   18. The display panel defect inspection apparatus according to claim 17, wherein the calculation means performs a calculation process of detecting the defect according to any one of claims 4 to 17 or evaluating the degree of the defect. A display panel manufacturing method comprising the display panel defect inspection method according to claim 1 in a manufacturing process.

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